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| import json | |
| import gradio as gr | |
| from nn_meter import load_latency_predictor | |
| cortexA76cpu_predictor = load_latency_predictor("cortexA76cpu_tflite21") | |
| adreno640gpu_predictor = load_latency_predictor("adreno640gpu_tflite21") | |
| adreno630gpu = load_latency_predictor("adreno630gpu_tflite21") | |
| myriadvpu_predictor = load_latency_predictor("myriadvpu_openvino2019r2") | |
| predictor_map = { | |
| "cortexA76cpu_tflite21": cortexA76cpu_predictor, | |
| "adreno640gpu_tflite21": adreno640gpu_predictor, | |
| "adreno630gpu_tflite21": adreno630gpu, | |
| "myriadvpu_openvino2019r2": myriadvpu_predictor | |
| } | |
| feature_for_kernel = { | |
| # remove the last two float | |
| "conv": ["HW", "CIN", "COUT", "KERNEL_SIZE", "STRIDES"], | |
| "dwconv": ["HW", "CIN", "COUT", "KERNEL_SIZE", "STRIDES"], | |
| "fc": ["CIN", "COUT"], | |
| # support up to 4 cin, if less than 4, the latter cin will be set to 0 | |
| "concat": ["HW", "CIN1", "CIN2", "CIN3", "CIN4"], | |
| # | |
| "maxpool": ["HW", "CIN", "COUT", "KERNEL_SIZE", "POOL_STRIDES"], | |
| "avgpool": ["HW", "CIN", "COUT", "KERNEL_SIZE", "POOL_STRIDES"], | |
| "split": ["HW", "CIN"], | |
| "channelshuffle": ["HW", "CIN"], | |
| "se": ["HW", "CIN"], | |
| "global-avgpool": ["HW", "CIN"], | |
| "bnrelu": ["HW", "CIN"], | |
| "bn": ["HW", "CIN"], | |
| "hswish": ["HW", "CIN"], | |
| "relu": ["HW", "CIN"], | |
| "addrelu": ["HW", "CIN1", "CIN2"], | |
| "add": ["HW", "CIN1", "CIN2"], | |
| } | |
| def get_type(str): | |
| operate_type = str.split("-")[0] | |
| if operate_type == 'global' or operate_type == 'gap': | |
| operate_type = 'global-avgpool' | |
| return operate_type | |
| def get_configuration(operate_type, value_arr): | |
| feature_arr = feature_for_kernel[operate_type] | |
| if operate_type == 'concat': | |
| configuration_arr = [] | |
| for i in range(len(feature_arr)): | |
| if value_arr[i] != 0: | |
| configuration_arr.append(feature_arr[i]+"="+str(value_arr[i])) | |
| else: | |
| break | |
| else: | |
| configuration_arr = [feature_arr[i]+"="+str(value_arr[i]) for i in range(min(len(feature_arr),len(value_arr)))] | |
| return ', '.join(configuration_arr) | |
| def data_process(data): | |
| new_data = [] | |
| for item in data: | |
| operate_type = get_type(item[1]) | |
| new_item = { | |
| "order": item[0], | |
| "type": operate_type, | |
| "configuration": get_configuration(operate_type, item[2]), | |
| "latency": item[3], | |
| "name": item[4], | |
| } | |
| new_data.append(new_item) | |
| return new_data | |
| def generate_html(hardware, latency, block_detail): | |
| data = data_process(block_detail) | |
| doc = """<html> | |
| <head> | |
| <meta http-equiv="content-type" content="text/html; charset=UTF-8" /> | |
| <meta name="viewport" content="width=device-width, | |
| initial-scale=1.0, maximum-scale=1.0, user-scalable=no" /> | |
| <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet"> | |
| <link href="https://unpkg.com/[email protected]/dist/bootstrap-table.min.css" rel="stylesheet"> | |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/font/bootstrap-icons.css"> | |
| <style> | |
| html { | |
| font-family: sans-serif; | |
| padding: 5px; | |
| } | |
| body { | |
| padding: 10px; | |
| font-size: 0.875rem; | |
| } | |
| #dataviz { | |
| width: 100%; | |
| height: 300px; | |
| position: relative; | |
| } | |
| #toolbar { | |
| margin-top: 10px; | |
| margin-bottom: 15px; | |
| display: flex; | |
| align-items: center; | |
| } | |
| input[type="number"]:focus-visible { | |
| outline: none; | |
| } | |
| .bootstrap-table .fixed-table-container .fixed-table-body { | |
| height: auto; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <h4 style="font-size: 1.5rem">Latency Analysis <i class="bi bi-question-circle" data-bs-container="body" data-bs-toggle="popover" data-bs-placement="right" style="font-size:1.2rem;"></i></h4> | |
| <div id="popoverInfo" style="display: none"> | |
| The latency results are empowered by Microsoft nn-Meter. For more technical details, please refer to the paper: <a href="https://dl.acm.org/doi/abs/10.1145/3529706.3529712" target="_blank">nn-METER: Towards Accurate Latency Prediction of DNN Inference on Diverse Edge Devices</a>. | |
| </div> | |
| <div id="toolbar"> | |
| <div style="display: flex;align-items: center;"> | |
| <span>Group By: </span> | |
| <select class="form-select" id="inputGroupBy" style="width: fit-content;margin-left: 5px;"> | |
| <option value="type">Operator Type</option> | |
| <option value="name">None</option> | |
| </select> | |
| </div> | |
| <div style="margin-left: 45px;margin-top:6px;display: flex;align-items: center;"> | |
| <div><label><input type="radio" name="quantity" value="all" class="quantity" checked> Show all</label></div> | |
| <div style="margin-left: 10px;"> | |
| <label><input type="radio" name="quantity" value="top" class="quantity"> Show top</label> | |
| <input type="number" value="10" min="1" style="width: 50px; border: none; | |
| border-bottom: 1px #aaa solid;" id="quantityNumber" disabled> | |
| </div> | |
| </div> | |
| </div> | |
| <div style="display: flex;"> | |
| <div id="dataviz"> </div> | |
| </div> | |
| <table id="table" data-search="true" data-search-align="left" data-pagination="true" data-page-size="30" data-page-list="[10, 20, 30, 50, 100, all]"> | |
| <thead> | |
| <tr> | |
| <th data-field="order" data-sortable="true">Excution Order</th> | |
| <th data-field="type" data-sortable="true">Operator Type</th> | |
| <th data-field="configuration">Configuration</th> | |
| <th data-field="latency" data-sortable="true">Latency (ms)</th> | |
| <th data-field="name" width="20%" data-sortable="true">Detail Operator</th> | |
| </tr> | |
| </thead> | |
| </table> | |
| <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/echarts.min.js" type="text/javascript"></script> | |
| <script src="https://cdn.jsdelivr.net/npm/jquery/dist/jquery.min.js"></script> | |
| <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"></script> | |
| <script src="https://unpkg.com/[email protected]/dist/bootstrap-table.min.js"></script> | |
| </body> | |
| <script> | |
| """ + f"""let rawData = {str(data).replace("'", '"')};""" + """ | |
| rawData.forEach(item => { | |
| item.name = item.name.split(";").join("; "); | |
| item.latency = Number(item.latency) ? Number(item.latency) : item.latency; | |
| }) | |
| // table | |
| let $table = $("#table"); | |
| $(function () { | |
| $table.bootstrapTable({ data: rawData }) | |
| }) | |
| // visualization | |
| const chartDom = document.getElementById("dataviz"); | |
| let myChart = echarts.init(chartDom); | |
| Array.prototype.groupBy = function (key) { | |
| return this.reduce(function (rv, x) { | |
| (rv[x[key]] = rv[x[key]] || []).push(x); | |
| return rv; | |
| }, {}); | |
| }; | |
| function processData(rawData, groupBy, quantity) { | |
| // transform data | |
| let seriesData = Object.entries(rawData.groupBy(groupBy)).map(([name, arr]) => { | |
| const value = arr.reduce((sum, curr) => sum + curr.latency, 0); | |
| const type = arr[0].type; | |
| return { name, value, type } | |
| }) | |
| .sort((a, b) => { | |
| return b.value - a.value | |
| }); | |
| if (quantity) { | |
| seriesData = seriesData.slice(0, quantity); | |
| } | |
| return { | |
| seriesData, | |
| legendData: seriesData.filter(d => Number(d.value)).map(d => d.name) | |
| }; | |
| } | |
| function formatNumber(num, fixed = 2){ | |
| if(Number(num.toFixed(fixed)) > 0){ | |
| return num.toFixed(fixed); | |
| }else{ | |
| return num.toPrecision(1); | |
| } | |
| } | |
| function render(data, groupBy) { | |
| const sum = data.seriesData.reduce(function (prev, current) { | |
| return prev + (Number(current.value) ? Number(current.value) : 0) | |
| }, 0); | |
| let option = { | |
| title: { | |
| text: """ + f"""`Total latency is {format(latency, '.4f')}(ms)`, | |
| subtext: `on Hardware {hardware}`,"""+""" | |
| left: "left", | |
| textStyle:{ | |
| fontSize: 14 | |
| } | |
| }, | |
| tooltip: { | |
| trigger: "item", | |
| formatter: (params) => groupBy==="name"? `<i>type:</i> ${params.data.type}<br><i>detail:</i> ${params.data.name}<br><b>${formatNumber(params.data.value)}</b><br><b>(${formatNumber(params.data.value / sum * 100)}%)</b>` : `${params.data.name}<br><b>${formatNumber(params.data.value)}</b><br><b>(${formatNumber(params.data.value / sum * 100)}%)</b>`, | |
| extraCssText: "max-width: 400px; white-space: break-spaces;" | |
| }, | |
| legend: { | |
| type: "scroll", | |
| orient: "vertical", | |
| right: "10%", | |
| top: "12%", | |
| bottom: "12%", | |
| data: data.legendData, | |
| formatter: (name) => { | |
| let arr = name.split(";"); | |
| return arr.length === 1 ? name : (arr[0]+"..."); | |
| }, | |
| tooltip: { | |
| show: true, | |
| formatter: (params) => { | |
| let datum = data.seriesData.find(d => d.name === params.name); | |
| return groupBy==="name"? `<i>type:</i> ${datum.type}<br><i>detail:</i> ${datum.name}<br><b>${formatNumber(datum.value)}</b><br><b>(${formatNumber(datum.value / sum * 100)}%)</b>` :`${datum.name}<br><b>${formatNumber(datum.value)}</b><br><b>(${formatNumber(datum.value / sum * 100)}%)</b>` | |
| }, | |
| position: (point, params, dom, rect, { contentSize, viewSize }) => [viewSize[0] * 0.4 - contentSize[0] * 0.5, viewSize[1] * 0.5 - contentSize[1] * 0.5] | |
| } | |
| }, | |
| series: [ | |
| { | |
| type: "pie", | |
| radius: ["40%", "75%"], | |
| center: ["40%", "50%"], | |
| data: data.seriesData, | |
| emphasis: { | |
| itemStyle: { | |
| shadowBlur: 10, | |
| shadowOffsetX: 0, | |
| shadowColor: "rgba(0, 0, 0, 0.5)" | |
| } | |
| }, label: { | |
| formatter: "{d}%", | |
| position: "inside", | |
| color: "#fff", | |
| }, | |
| } | |
| ], | |
| color: ["#4e79a7", "#f28e2c", "#e15759", "#76b7b2", "#59a14f", "#edc949", "#af7aa1", "#ff9da7", "#9c755f", "#bab0ab"] | |
| }; | |
| myChart.dispose(); | |
| myChart = echarts.init(chartDom); | |
| myChart.setOption(option); | |
| myChart.on("selectchanged", function(params){ | |
| const index = params.fromActionPayload.dataIndexInside; | |
| const text = data.seriesData[index].name; | |
| $table.bootstrapTable("resetSearch", text); | |
| }); | |
| myChart.on("legendselectchanged", function(params) { | |
| suppressSelection(myChart, params); | |
| }); | |
| function suppressSelection(chart, params) { | |
| chart.setOption({ animation: false }); | |
| // Re-select what the user unselected | |
| chart.dispatchAction({ | |
| type: "legendSelect", | |
| name: params.name | |
| }); | |
| chart.setOption({ animation: true }); | |
| } | |
| } | |
| // config | |
| let groupBy = "type"; | |
| let quantityNumber = 10; | |
| let showAll = true; | |
| render(processData(rawData, groupBy), groupBy); | |
| function redraw() { | |
| render(processData(rawData, groupBy, showAll ? null : quantityNumber), groupBy); | |
| } | |
| // change groupby | |
| document.getElementById("inputGroupBy") | |
| .addEventListener("change", function () { | |
| groupBy = this.value; | |
| redraw(); | |
| }); | |
| // change the model of show | |
| function changeShowModel() { | |
| if (this.value === "top") { | |
| document.getElementById("quantityNumber").disabled = false; | |
| showAll = false; | |
| } else { | |
| document.getElementById("quantityNumber").disabled = true; | |
| showAll = true; | |
| } | |
| redraw(); | |
| } | |
| let items = Object.values(document.getElementsByClassName("quantity")) | |
| .forEach(item => item.addEventListener("change", changeShowModel)); | |
| // change the number of show | |
| document.getElementById("quantityNumber") | |
| .addEventListener("change", function () { | |
| quantityNumber = this.value; | |
| redraw(); | |
| }) | |
| // enable popover | |
| const popoverTriggerList = document.querySelectorAll(`[data-bs-toggle="popover"]`) | |
| const popoverList = [...popoverTriggerList].map(popoverTriggerEl => new bootstrap.Popover(popoverTriggerEl, { | |
| html : true, | |
| content: function() { | |
| return $("#popoverInfo").html(); | |
| } | |
| })); | |
| </script> | |
| </html> | |
| """ | |
| return f"""<iframe style="width: 100%; height: 480px" name="result" allow="midi; geolocation; microphone; camera; display-capture; encrypted-media;" sandbox="allow-modals allow-forms allow-scripts allow-same-origin allow-popups allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" allowpaymentrequest="" frameborder="0" srcdoc='{doc}'></iframe>""" | |
| def generate_error_html(massage): | |
| return f"""<div style="color:#842029;background: #f8d7da;padding: 10px;border-radius: 10px; margin-top: 15px;"><b>nn-meter meets an error in latency prediction</b>: {massage}</div> | |
| <div style="padding: 10px;">If you have any questions about the result, you can open new issues in <a href="https://github.com/microsoft/nn-Meter" target="_blank" style="color:#2563eb">nn-meter Git repository</a>.</div> | |
| """ | |
| def get_latency(model, hardware_name): | |
| if model == None: | |
| return generate_error_html("Please upload a model file or select one example below.") | |
| model = model.name | |
| if hardware_name == '': | |
| return generate_error_html("Please select a device.") | |
| predictor = predictor_map[hardware_name] | |
| if model.endswith("onnx"): | |
| model_type = "onnx" | |
| elif model.endswith("pb"): | |
| model_type = "pb" | |
| else: | |
| model_type = "nnmeter-ir" | |
| try: | |
| model_latency, block_detail = predictor.detailed_predict(model, model_type) | |
| return generate_html(hardware_name, model_latency, block_detail) | |
| except Exception as e: | |
| return generate_error_html(repr(e)) | |
| title = "Interactive demo: nn-Meter (Draft Version)" | |
| description = "Demo for Microsoft's nn-Meter, a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices. To use it, simply upload a model file, or use one of the example below and click ‘submit’. Results will show up in a few seconds." | |
| article = "<p style='text-align: center'><a href='https://dl.acm.org/doi/10.1145/3458864.3467882'>nn-Meter: towards accurate latency prediction of deep-learning model inference on diverse edge devices</a> | <a href='https://github.com/microsoft/nn-Meter'>Github Repo</a></p>" | |
| examples =[ | |
| ["samples/mobilenetv3small_0.pb", "cortexA76cpu_tflite21"], | |
| ["samples/mobilenetv3small_0.onnx", "adreno640gpu_tflite21"], | |
| ["samples/mobilenetv3small_0.json", "adreno630gpu_tflite21"] | |
| ] | |
| inputs = [ | |
| gr.inputs.File(label="Model File"), | |
| gr.inputs.Radio(choices=["cortexA76cpu_tflite21", "adreno640gpu_tflite21", "adreno630gpu_tflite21", "myriadvpu_openvino2019r2"], label="Device"), | |
| ] | |
| outputs = gr.outputs.HTML() | |
| iface = gr.Interface(fn=get_latency, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| allow_flagging="auto", | |
| css=""" | |
| div[id="6"] { | |
| flex-direction: column; | |
| } | |
| div[id="12"] { | |
| margin-left: 0px !important; | |
| margin-top: 0.75em !important; | |
| } | |
| div[id="12"] iframe{ | |
| height: 80vh !important; | |
| } | |
| """) | |
| iface.launch() | |